private IRatings ReadData(string path, IMapping usersMap, IMapping itemsMap)
{
var ratings = new MyMediaLite.Data.Ratings();
File.ReadAllLines(path).Select(l =>
{
var parts = l.Split(new string[] { "::" }, StringSplitOptions.RemoveEmptyEntries);
return(new { User = parts[0], Item = parts[1], Rating = float.Parse(parts[2]) });
}).ToList()
.ForEach(r =>
ratings.Add(usersMap.ToInternalID(r.User), itemsMap.ToInternalID(r.Item), r.Rating));
return(ratings);
}
public void Train(IEnumerable <ItemRating> trainSet)
{
Console.WriteLine("Training...");
var ratings = new MyMediaLite.Data.Ratings();
// Convert trainset to MyMediaLite trianset format
foreach (var itemRating in trainSet)
{
ratings.Add(_usersMap.ToInternalID(itemRating.User.Id), _itemsMap.ToInternalID(itemRating.Item.Id), itemRating.Rating);
}
_ratingPredictor.Ratings = ratings;
_ratingPredictor.Train();
_isTrained = true;
}
public static void Main(string[] args)
{
// TODO add random seed
// TODO report per-user times
string data_file = args[0];
string method = args[1];
string options = args[2];
int num_test_users = int.Parse(args[3]);
// load the data
var all_data = RatingData.Read(data_file);
// TODO randomize
var test_users = new HashSet<int>(Enumerable.Range(0, num_test_users));
var update_indices = new List<int>();
var eval_indices = new List<int>();
foreach (int user_id in test_users)
if (all_data.ByUser[user_id].Count > 1)
{
var user_indices = all_data.ByUser[user_id];
for (int i = 0; i < user_indices.Count - 1; i++)
update_indices.Add(user_indices[i]);
for (int i = user_indices.Count - 1; i < user_indices.Count; i++)
eval_indices.Add(user_indices[i]);
}
var training_indices = new List<int>();
for (int i = 0; i < all_data.Count; i++)
if (!test_users.Contains(all_data.Users[i]))
training_indices.Add(i);
var training_data = new MyMediaLite.Data.Ratings();
foreach (int i in training_indices)
training_data.Add(all_data.Users[i], all_data.Items[i], all_data[i]);
var update_data = new RatingsProxy(all_data, update_indices);
var eval_data = new RatingsProxy(all_data, eval_indices);
Console.Write(training_data.Statistics());
Console.Write(update_data.Statistics());
Console.Write(eval_data.Statistics());
// prepare recommender
RatingPredictor recommender = Recommender.CreateRatingPredictor(method);
recommender.Configure(options);
recommender.Ratings = training_data;
Console.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
Console.WriteLine("recommender: {0}", recommender);
recommender.Train();
// I. complete retraining
Console.WriteLine(
"complete training: {0}",
recommender.EvaluateFoldInCompleteRetraining(update_data, eval_data));
// II. online updates
Console.WriteLine(
"incremental training: {0}",
((IncrementalRatingPredictor)recommender).EvaluateFoldInIncrementalTraining(update_data, eval_data));
// III. fold-in
Console.WriteLine(
"fold-in: {0}",
((IFoldInRatingPredictor)recommender).EvaluateFoldIn(update_data, eval_data));
}
public static void Main(string[] args)
{
// TODO add random seed
// TODO report per-user times
string data_file = args[0];
string method = args[1];
string options = args[2];
int num_test_users = int.Parse(args[3]);
// load the data
var all_data = RatingData.Read(data_file);
// TODO randomize
var test_users = new HashSet <int>(Enumerable.Range(0, num_test_users));
var update_indices = new List <int>();
var eval_indices = new List <int>();
foreach (int user_id in test_users)
{
if (all_data.ByUser[user_id].Count > 1)
{
var user_indices = all_data.ByUser[user_id];
for (int i = 0; i < user_indices.Count - 1; i++)
{
update_indices.Add(user_indices[i]);
}
for (int i = user_indices.Count - 1; i < user_indices.Count; i++)
{
eval_indices.Add(user_indices[i]);
}
}
}
var training_indices = new List <int>();
for (int i = 0; i < all_data.Count; i++)
{
if (!test_users.Contains(all_data.Users[i]))
{
training_indices.Add(i);
}
}
var training_data = new MyMediaLite.Data.Ratings();
foreach (int i in training_indices)
{
training_data.Add(all_data.Users[i], all_data.Items[i], all_data[i]);
}
var update_data = new RatingsProxy(all_data, update_indices);
var eval_data = new RatingsProxy(all_data, eval_indices);
Console.Write(training_data.Statistics());
Console.Write(update_data.Statistics());
Console.Write(eval_data.Statistics());
// prepare recommender
RatingPredictor recommender = method.CreateRatingPredictor();
recommender.Configure(options);
recommender.Ratings = training_data;
Console.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
Console.WriteLine("recommender: {0}", recommender);
recommender.Train();
// I. complete retraining
Console.WriteLine(
"complete training: {0}",
recommender.EvaluateFoldInCompleteRetraining(update_data, eval_data));
// II. online updates
Console.WriteLine(
"incremental training: {0}",
((IncrementalRatingPredictor)recommender).EvaluateFoldInIncrementalTraining(update_data, eval_data));
// III. fold-in
Console.WriteLine(
"fold-in: {0}",
((IFoldInRatingPredictor)recommender).EvaluateFoldIn(update_data, eval_data));
}
